Organic mulches in highbush blueberries alter beetle (Coleoptera) community composition and improve functional group abundance and diversity
Bibliographic record
Abstract
Abstract Horticultural practices may impact invertebrates in agroecosystems, particularly natural enemies. Impacts can be better understood by grouping organisms functionally or using morphological traits in addition to taxonomic determinations. We compared the effects of mulch type (compost, pine needles, unmulched) and weeding strategy (weeded, unweeded) on beetle (Coleoptera) communities in highbush blueberries, focusing on early‐season captures that reflected overwintering habitat. Beetle diversity was similar between plot types, although functional grouping revealed differences as a result of mulching but not weeding. Predatory and granivorous Carabidae were most abundant in unmulched plots, mycetophages were most abundant in pine needles, and saprophages were most abundant in compost. Predatory Staphylinidae were most diverse in compost plots, and the diversity of granivores was greatest in unmulched plots. Carabid biomass was greater in unmulched than compost mulched plots partly as a result of larger beetle size. Beetle communities in unmulched and pine needles mulched plots were more similar than those in compost mulched plots. A combination of compost mulched and unmulched areas should benefit all predatory taxa, although mulch use for pest control will need to be evaluated within the context of other production goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".